Autonomous driving has quickly become an area of interest for vehicle manufactures and navigation and mapping service providers. One particular area of interest is the use of computer vision to enable mapping and sensing of a vehicle's environment to support autonomous or semi-autonomous operation. Advances in available computing power have enabled this mapping and sensing to approach or achieve real-time operation through, e.g., machine learning (e.g., neural networks). As a result, one application of computer vision techniques in autonomous driving is localization of the vehicle with respect to known reference marks objects on or near a roadway. Accordingly, service providers face significant technical challenges when applying computer vision to detect such objects (e.g., lane lines, signs, or other surface markings) from a captured image, particularly when attempting to detect the objects in real-time or near real-time.